The primary goal of Professor Sheldon’s research is to develop algorithms to understand and make decisions about the environment using large data sets. He seeks to answer foundational questions (what are the general models and principles that underlie big data problems in ecology?) and also to build applications that transform large-scale data resources into scientific knowledge and policy. Some examples of his work include: spatial optimization to conserve endangered species, continent-scale modeling of bird migration, and biological interpretation of weather radar data across the US. Methodologically, Professor Sheldon’s primary interests are machine learning, probabilistic inference, and network modeling. His work has contributed broadly applicable new approaches for reasoning about aggregate data in probabilistic graphical models, and for optimization of diffusion processes in networks.